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DTSTART;TZID=America/New_York:20201012T120000
DTEND;TZID=America/New_York:20201012T130000
DTSTAMP:20260407T151506
CREATED:20200908T165615Z
LAST-MODIFIED:20200908T165615Z
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SUMMARY:PSOC Webinar: "Manipulating Macrophage Signaling" (Meghan Morrissey)
DESCRIPTION:“Manipulating Macrophage Signaling – How macrophages measure ‘Eat me’ and ‘Don’t eat me’ signals” \nPhysical Sciences in Oncology Center PSOC@Penn \nFall 2020 Webinar Series Mondays @ Noon (EST) \nFor webinar links\, please contact manu@seas.upenn.edu
URL:https://seasevents.nmsdev7.com/event/psoc-webinar-manipulating-macrophage-signaling-meghan-morrissey/
LOCATION:PA
CATEGORIES:Seminar
ORGANIZER;CN="PSOC":MAILTO:manu@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201013T103000
DTEND;TZID=America/New_York:20201013T120000
DTSTAMP:20260407T151506
CREATED:20200831T133050Z
LAST-MODIFIED:20200831T133050Z
UID:10006462-1602585000-1602590400@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: "Real-Time Reduced Order Modeling using Time-Dependent Basis: Applications in Turbulent Flows and Combustion"
DESCRIPTION:The question of “what set of basis functions should be used?” is of fundamental importance to scientific computing. The performance of different choices of basis is assessed primarily by the rate of convergence and robustness. However\, when the one-dimensional basis is extended to higher dimensions d\, the computational complexity increases exponentially with respect to d. This fundamental challenge has been dubbed the curse of dimensionality and it is one of the greatest impediments to solving many important problems in science and engineering. One of the most promising approaches to beat the curse of dimensionality is exploiting correlations of the solution between various dimensions. In this work\, a framework for extracting and exploiting correlated structures is presented. The correlated structures take the form a set of time-dependent basis. This framework is based on a variational principle whose optimality condition leads to closed form evolution equations for a set time-dependent bases. The bases are directly extracted from the model (e.g. partial differential equations) or from the streaming data. Several applications will be presented including computing sensitivities in chaotic dynamical systems and real-time reduced order modeling of turbulent reactive flows.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-real-time-reduced-order-modeling-using-time-dependent-basis-applications-in-turbulent-flows-and-combustion/
LOCATION:Zoom – Email MEAM for Link\, peterlit@seas.upenn.edu
CATEGORIES:Seminar
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201013T120000
DTEND;TZID=America/New_York:20201013T130000
DTSTAMP:20260407T151506
CREATED:20200918T005455Z
LAST-MODIFIED:20200918T005455Z
UID:10006506-1602590400-1602594000@seasevents.nmsdev7.com
SUMMARY:ESE Seminar: "Data-driven Decision Making and Estimation in CPS/IoT Networks"
DESCRIPTION:The modern instantiation of a sensor network is a cyberphysical (CPS) system where CPS subsystems can be interconnected by a shared communication network of limited bandwidth. A common problem in CPS networks is the sensing and communication of spatio-temporal signals. However\, there are fundamental differences between estimation (sensing) and communication. For example\, the type of signal one would design to optimize sensing is very different from that for optimized communication. In this talk\, we explore some of these differences and discuss how joint communication and sensing should occur in different problem settings. In particular\, we examine problems where multiple sensors make observations and must share the communication medium to transmit these signals to a fusion center that will endeavor to perform remote estimation all of the sensed signals. A new class of remote estimation problems\, where the communication resources are allocated dynamically based on the observations at the sensors\, rather than purely on their statistical description is examined. We address the optimal design of a collision avoidance policy by selecting the most informative sensor to transmit at a time. First\, we will establish person-by-person optimal policies for the scheduling of sensors making Gaussian observations. Then\, we will show how our theoretical results can be applied to design scheduling policies where the joint probability density of the observations is unknown using machine learning techniques. We will extend our results to the case when the scheduler uses an energy harvesting battery as well as to the case of purely decentralized decision making. Time permitting\, applications to the case of microbial decision making in quorum sensing networks will be discussed. \n 
URL:https://seasevents.nmsdev7.com/event/ese-seminar-data-driven-decision-making-and-estimation-in-cps-iot-networks/
LOCATION:Zoom – Email ESE for Link jbatter@seas.upenn.edu
CATEGORIES:Seminar,Distinguished Lecture,Faculty,Colloquium,Graduate,Undergraduate
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201014T150000
DTEND;TZID=America/New_York:20201014T160000
DTSTAMP:20260407T151506
CREATED:20200921T212930Z
LAST-MODIFIED:20200921T212930Z
UID:10006509-1602687600-1602691200@seasevents.nmsdev7.com
SUMMARY:CBE Seminar: "Fuels and Chemicals Decarbonized"
DESCRIPTION:Abstract \nFuels based on hydrocarbons\, taken together with the processing of raw hydrocarbons into chemicals\, account for about half of the United States’ CO2 emissions. Efforts to lessen the CO2 footprint of these activities have the potential to make a significant impact on total greenhouse emissions. We focus on taking CO2 and – with energy supplied by low carbon/renewable electricity – turning it into fuels and fuel additives such as methane\, ethanol\, and propanol. I will update on progress in the energy efficiency and intensity (activity) of these processes. We also seek to advance the electrosynthesis of renewable chemicals such as ethylene and – more recently on the anodic side – ethylene glycol and ethylene oxide.
URL:https://seasevents.nmsdev7.com/event/cbe-seminar-fuels-and-chemicals-decarbonized/
LOCATION:Zoom – Email CBE for link
CATEGORIES:Seminar
ORGANIZER;CN="Chemical and Biomolecular Engineering":MAILTO:cbemail@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201015T104500
DTEND;TZID=America/New_York:20201015T114500
DTSTAMP:20260407T151506
CREATED:20200828T153254Z
LAST-MODIFIED:20200828T153254Z
UID:10006455-1602758700-1602762300@seasevents.nmsdev7.com
SUMMARY:MSE Seminar: "The interplay between phase transformation and deformation mechanisms in multicomponent metal alloys"
DESCRIPTION:Phase prediction in multicomponent alloys remains one of the most fundamental challenges. Navigating the vast compositional space of these alloys requires a predictive capability to efficiently guide alloy discovery and microstructure design. Recently\, Dr. Ghazisaeidi has developed a Multicell Monte Carlo (MC)^2 method\, based on first-principles calculations\, to study phase formation in multicomponent alloys. This method is particularly powerful when applied to multicomponent systems\, for which phase diagrams do not exist. First\, she introduce the (MC)^2 method and present its successful prediction of the stable phases of known binary systems. Next\, she will present the application of (MC)^2 to “high entropy” alloys and discuss the effect of emerging stable phases on deformation mechanisms and consequently the overall mechanical behavior.
URL:https://seasevents.nmsdev7.com/event/mse-seminar-characterization-of-complex-eutectic-microstructures-2/
LOCATION:PA
ORGANIZER;CN="Materials Science and Engineering":MAILTO:johnruss@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201016T100000
DTEND;TZID=America/New_York:20201016T110000
DTSTAMP:20260407T151506
CREATED:20200924T194101Z
LAST-MODIFIED:20200924T194101Z
UID:10006514-1602842400-1602846000@seasevents.nmsdev7.com
SUMMARY:SIG Seminar: "Uncertainty-Driven Geometry Reconstruction and Understanding"
DESCRIPTION:Choosing suitable data representations is one of the most critical topics when installing machine learning on 3D data. This talk discusses several recent works on utilizing uncertainty as a lens to develop suitable data representations and formulations for geometry reconstruction and understanding. We will discuss both theoretical results and applications in multi-scan registration\, pose estimation\, and scene understanding.
URL:https://seasevents.nmsdev7.com/event/sig-seminar-uncertainty-driven-geometry-reconstruction-and-understanding/
LOCATION:Zoom – Email CIS for link\, cherylh@cis.upenn.edu
ORGANIZER;CN="Computer and Information Science":MAILTO:cherylh@cis.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201017T100000
DTEND;TZID=America/New_York:20201017T160000
DTSTAMP:20260407T151506
CREATED:20200902T132200Z
LAST-MODIFIED:20200902T132200Z
UID:10006473-1602928800-1602950400@seasevents.nmsdev7.com
SUMMARY:PICS: "C++ Workshop"
DESCRIPTION:The Penn Institute for Computational Science (PICS) is proud to host a C++ workshop on Saturday\, October 17 from 10:00am – 3:30pm via Zoom. This one day workshop will teach students the basic skills needed to program in C++. We ask that anyone who attends has at least one year of prior programming experience in any language. \nYou must click here and RSVP to attend this event. The class will be capped at 30 people. Students enrolled in the PICS certificate program will receive priority enrollment.
URL:https://seasevents.nmsdev7.com/event/pics-c-workshop/
LOCATION:Zoom – Email CIS for link\, cherylh@cis.upenn.edu
CATEGORIES:Workshop
ORGANIZER;CN="Penn Institute for Computational Science (PICS)":MAILTO:dkparks@seas.upenn.edu
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